Haris Kremo

Adaptive beaconing for congestion and awareness control in vehicular networks

Cooperative vehicular networks require the exchange of positioning and basic status information between neighboring nodes to support higher layer protocols and applications, including active safety applications. The information exchange is based on the periodic transmission/reception of 1-hop broadcast messages on the so called control channel. The dynamic adaptation of the transmission parameters of such messages will be key for the reliable and efficient operation of the system. On one hand, congestion control protocols need to be applied to control the channel load, typically through the adaptation of the transmission parameters based on certain channel load metrics. On the other hand, awareness control protocols are also required to adequately support cooperative vehicular applications. Such protocols typically adapt the transmission parameters of periodic broadcast messages to ensure each vehicles capacity to detect, and possibly communicate, with the relevant vehicles and infrastructure nodes present in its local neighborhood. To date, congestion and awareness control protocols have been normally designed and evaluated separately, although both will be required for the reliable and efficient operation of the system. To this aim, this paper proposes and evaluates INTERN, a new control protocol that integrates two congestion and awareness control processes. The simulation results obtained demonstrate that INTERN is able to satisfy the applications requirements of all vehicles, while effectively controlling the channel load.

Throughput-Efficient Channel Allocation in Multi-Channel Cognitive Vehicular Networks

Recent studies show that the Dedicated Short Range Communication (DSRC) band allocated to vehicular networks is insufficient to carry the wireless traffic load generated by emerging applications for vehicular systems. A promising bandwidth expansion possibility presents itself through the release of large TV band spectra by FCC for cognitive access. One of the primary challenges of the so-called TV White Space (TVWS) access in vehicular networks is the design of efficient channel allocation mechanisms in face of high vehicular mobility and spatial-temporal variations of TVWS. In this paper, we address the channel allocation problem for multi-channel cognitive vehicular networks with the objective of system-wide throughput maximization. We show that the problem is a NP-hard combinatorial optimization problem, to which we present two solution approaches. We first propose a probabilistic polynomial-time (1 - 1/e)-approximation algorithm based on linear programming. Next, we prove that our objective function can be written as a submodular set function, based on which we develop a deterministic polynomial-time constant-factor approximation algorithm with a more favorable time complexity. Finally, we show the efficacy of our algorithms through numerical examples.

On Detecting Spectrum Opportunities for Cognitive Vehicular Networks in the TV White Space

We overview the challenges related to spectrum awareness in the vehicular environment, with emphasis on awareness in the TV licensed band. In the vehicular environment the cognitive radio can help to: 1) satisfy capacity demand for Intelligent Transportation Systems (ITS) applications; and 2) offload time insensitive applications from the ITS dedicated spectrum. However, using simple propagation models we show that neither sensing, nor geolocation database lookup alone can provide sufficient incumbent protection. Collaboration among the sensors to take advantage of spatial diversity is difficult due to the rapidly changing network topology. Nevertheless, mobility provides the opportunity to use time diversity at each sensor. We also discuss the influence of sensing subsystem design on the vehicular cognitive network medium access (MAC) sublayer. Whenever appropriate, we evaluate applicability of the requirements imposed by the Federal Communications Commission (FCC) and the IEEE 802.22 standard to the cognitive vehicular networks.

Spectrum sharing methods for the coexistence of multiple RF systems: A survey

Abstract Due to the explosive growth of wireless devices and wireless traffic, the spectrum scarcity problem is becoming more urgent in numerous Radio Frequency (RF) systems. At the same time, many studies have shown that spectrum resources allocated to various existing RF systems are largely underutilized. As a potential solution to this spectrum scarcity problem, spectrum sharing among multiple, potentially dissimilar RF systems has been proposed. However, such spectrum sharing solutions are challenging to develop due to the lack of efficient coordination schemes and potentially different PHY/MAC properties. In this paper, we investigate existing spectrum sharing methods facilitating coexistence of various RF systems. The cognitive radio technique, which has been the subject of various surveys, constitutes a subset of our wider scope. We study more general coexistence scenarios and methods such as coexistence of communication systems with similar priorities, utilizing similar or different protocols or standards, as well as the coexistence of communication and non-communication systems using the same spectral resources. Finally, we explore open research issues on the spectrum sharing methods as well as potential approaches to resolving these issues.

Distributed autonomous multi-hop vehicle-to-vehicle communications over TV white space

This paper presents design and experimental evaluation of a distributed autonomous multi-hop vehicle-to-vehicle (V2V) communication system over TV white space performed in Japan. We propose the two-layer control channel model, which consists of the Zone Aware Control Channel (ZACC) and the Swarm Aware Control Channel (SACC), to establish the multi-hop network. Several vehicles construct a swarm using location and direction information shared through ZACC, and share route and channel information, and available white space information through SACC. To evaluate the system we carried out field experiments with swarm made of three vehicles in a convoy. As the test case application, the leading vehicle sends real-time speed and sudden break information to the rear vehicle, while the vehicle in between acts as the relay. The vehicles observe channel occupancy via energy detection and agree on the control and the data channels autonomously. For coarse synchronization of quiet periods for sensing we use GPS driven oscillators, and introduce a time margin to accommodate for remaining drift. When a primary user is detected in any of the borrowed channels, the vehicles switch to a vacant channel without disrupting the ongoing multi-hop communication. We present results of the experiments in terms of the time to establish control channel, channel switching time, and throughput.

Field tests and indoor emulation of distributed autonomous multi-hop vehicle-to-vehicle communications over TV white space

This paper presents design and experimental evaluation of a distributed autonomous multi-hop vehicle-to-vehicle (V2V) communication system over TV white space performed in Japan. We propose the two-layer control channel model, which consists of the Zone Aware Control Channel (ZACC) and the Swarm Aware Control Channel (SACC), to establish the multi-hop network. Several vehicles construct a swarm using location and direction information shared through ZACC, and share route and channel information, and available white space information through SACC. To evaluate the system we carried out field experiments with swarm made of three vehicles in a convoy. As the test case application, the leading vehicle sends real-time speed and sudden break information to the rear vehicle, while the vehicle in between acts as the relay. The vehicles observe channel occupancy via energy detection and agree on the control and the data channels autonomously. For coarse synchronization of quiet periods for sensing we use GPS driven oscillators, and introduce a time margin to accommodate for remaining drift. When a primary user is detected in any of the borrowed channels, the vehicles switch to a vacant channel without disrupting the ongoing multi-hop communication. We present results of the experiments in terms of the time to establish control channel, channel switching time, and throughput.

A survey of MAC issues for TV white space access

The opening of TV white space (TVWS) bands for cognitive access is one of the first tangible steps to solve spectrum scarcity problem in current wireless networks. However, this has also raised many new challenges to efficiently use the TVWS spectrum. One of the primary challenges is the design of efficient Medium Access Control (MAC) protocols that conform to various rules imposed to protect primary users as well as accommodate spatio-temporal variations of the TVWS spectrum. This article presents MAC-related challenges related to cognitive access to TVWS, discusses potential approaches to overcoming these challenges, and investigates open research issues. It also reviews regulatory activities in several countries and worldwide standardization efforts for TVWS access.

Vehicles as Information Hubs During Disasters: Glueing Wi-Fi to TV White Space to Cellular Networks

This paper is a brief description of the small scale field demonstration we presented at the ITS World Congress 2013 in Tokyo. We showed that during disasters vehicles can act as information hubs conveying human or machine centric information from an area where the telecommunications network is disrupted, to an area where the telecommunications infrastructure is available. The demonstration was a combination of different means of communication technologies including Wi-Fi, TV white space, cellular networks, and the movement of the vehicles themselves. Use of the TV white space for inter-vehicle communications was the first trial carried out in any metropolitan area in the world.

Optimal spectrum utilization in joint automotive radar and communication networks

Due to rapid growth of wireless traffic demands in vehicular networks, spectrum scarcity is becoming urgent in the Dedicated Short Range Communication (DSRC) band. One solution is reusing automotive radar bands without degrading radar performance. Despite having massive bandwidths, imaging accuracy of automotive radars is still low due to correlations between sequential target observations of single radar. A solution is that vehicles exchange imaging information through vehicle-to-vehicle communications. Since observations of different vehicles are less correlated, Joint Automotive Radar and Communication (JARC) network is able to improve imaging accuracy. More importantly, some spectrum resources can be left to alleviate the DSRC spectrum scarcity problem. In this paper, we derive the Cramer-Rao bound for parameter estimation in JARC networks. Then, we formulate the spectrum utilization problem as an NP-complete integer quadratic program, to which we propose an optimal (in expectation) algorithm with low complexity. Finally, efficacy of the algorithm is illustrated through numerical results.

Enabling coexistence of cognitive vehicular networks and IEEE 802.22 networks via optimal resource allocation

Many studies show that the Dedicated Short Range Communication band is insufficient to carry increasing wireless data traffic in vehicular networks. The release of large TV spectra by FCC for cognitive access provides additional spectrum resources to solve the spectrum scarcity problem. However, FCC allows fixed devices to use high transmitting powers, while requiring portable devices to use significantly lower powers. This power asymmetry policy leads to a challenging coexistence environment for portable (e.g., vehicular) and fixed (e.g., IEEE 802.22) networks. In this paper, we address the coexistence problem between vehicular and 802.22 networks via resource allocation. We show that the problem is an NP-hard mixed-integer nonlinear programming problem, to which we propose two algorithms. First, we convert it to a convex programming problem, and propose a near-optimal primal-dual algorithm. Next, we reformulate the problem as a packing problem, and present a constant-factor approximation algorithm. Finally, we evaluate the algorithms through numerical examples.